HAA500: Human-Centric Atomic Action Dataset with Curated Videos

TL;DR

HAA500 is a new, detailed human-centric atomic action dataset with 500 classes and over 591K frames, designed to improve action recognition by focusing on fine-grained, consistent human movements.

Contribution

The paper introduces HAA500, a curated dataset of atomic actions with high-quality annotations, enabling more precise and scalable action recognition research.

Findings

HAA500 improves action recognition accuracy through detailed human pose annotations.

The dataset's atomic and human-centric design enhances model training and generalization.

Cross-data validation shows HAA500's effectiveness in real-world scenarios.

Abstract

We contribute HAA500, a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591K labeled frames. To minimize ambiguities in action classification, HAA500 consists of highly diversified classes of fine-grained atomic actions, where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball". Thus HAA500 is different from existing atomic action datasets, where coarse-grained atomic actions were labeled with coarse action-verbs such as "Throw". HAA500 has been carefully curated to capture the precise movement of human figures with little class-irrelevant motions or spatio-temporal label noises. The advantages of HAA500 are fourfold: 1) human-centric actions with a high average of 69.7% detectable joints for the relevant human poses; 2) high scalability since adding a new class can be done under 20-60 minutes; 3) curated videos capturing essential elements of an atomic action without irrelevant frames; 4) fine-grained atomic action classes. Our extensive experiments including cross-data validation using datasets collected in the wild demonstrate the clear benefits of human-centric and atomic characteristics of HAA500, which enable training even a baseline deep learning model to improve prediction by attending to atomic human poses. We detail the HAA500 dataset statistics and collection methodology and compare quantitatively with existing action recognition datasets.